Quantification of lipoprotein subfractions by ion mobility

Abstract

Provided herein are methods for quantifying lipoprotein subfractions by ion mobility. Also provided herein are methods for diagnosing or predicting insulin resistance in a patient in need thereof (e.g., diabetic and / or prediabetic patients), comprising measuring lipoprotein subfraction levels in a patient sample by ion mobility.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 350,789, filed on June 9, 2022, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present invention relates to the identification and quantification of lipoprotein subfractions by ion mobility and determining the risk of insulin resistance. Background Art

[0004] Insulin resistance (IR) is associated with lipid and lipoprotein abnormalities, including high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C), which lead to an increased risk of atherosclerotic cardiovascular disease. Direct measurement of IR is labor intensive and cannot be performed in a clinical setting. Therefore, an efficient method to determine the risk of insulin resistance is needed. Summary of the invention

[0005] On the one hand, the present invention provides a method for diagnosing or predicting insulin resistance in a patient in need (e.g., diabetes and / or prediabetes patient), the method comprising measuring the lipoprotein subfraction levels in a patient sample by ion mobility. Also provided is a method for determining the amount of the lipoprotein subfractions in a sample, the method comprising determining the amount of the lipoprotein subfractions in a sample by ion mobility.

[0006] In certain embodiments, the methods provided herein comprise lipoprotein subfractions in a sample by ion mobility.

[0007] In some embodiments, provided herein are insulin resistance scores (RS) and / or the probability of developing insulin resistance P(IR) based on the levels of lipoprotein subfractions measured by the methods provided herein.

[0008] In some embodiments, the present invention provides an insulin resistance score (RS) and / or a probability of developing insulin resistance P(IR) based on the levels of lipoprotein subfractions and triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) levels measured by the methods provided herein.

[0009] In some embodiments, the present invention provides an insulin resistance score (RS) and / or the probability of developing insulin resistance P(IR) based on the levels of lipoprotein subfractions and triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) levels measured by the methods provided herein and in combination with sex, race, ethnicity and body mass index (BMI) measured by standard methods.

[0010] In certain embodiments, insulin resistance is diagnosed according to the level of lipoprotein subfractions measured by the method provided herein. In certain embodiments, insulin resistance is diagnosed according to the level of lipoprotein subfractions measured by the method provided herein and triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) levels. In certain embodiments, insulin resistance is diagnosed by the level of lipoprotein subfractions measured by the method provided herein and triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) levels and in combination with sex, race, ethnicity and body mass index (BMI) measured by standard methods.

[0011] As used herein, the singular forms "a", "an", and "the" include plural references unless otherwise indicated. Thus, for example, reference to "a protein" includes a plurality of protein molecules.

[0012] The term "purification" or "purifying" refers to the procedure of enriching the amount of one or more analytes of interest relative to other components in the sample that may interfere with the detection of the analyte of interest. Although not required, "purification" can completely remove all interfering components, or even all materials except the analyte of interest. Purifying a sample by various methods can allow for a relative reduction in one or more interfering substances, for example, one or more substances that may or may not interfere with the detection of a selected parent ion or daughter ion by mass spectrometry. When the term relative reduction is used, the relative reduction does not require that any substance present with the analyte of interest in the material to be purified is completely removed by purification.

[0013] The term "sample" refers to any sample that may contain an analyte of interest. As used herein, the term "body fluid" means any fluid that can be isolated from an individual's body. For example, a "body fluid" may include blood, plasma, serum, bile, saliva, urine, tears, sweat, etc. In a preferred embodiment, the sample comprises a body fluid sample; preferably plasma or serum.

[0014] The "amount" of an analyte in a bodily fluid sample generally refers to an absolute value reflecting the mass of the analyte detectable in the sample volume. However, the amount also encompasses a relative amount compared to another amount of an analyte. For example, the amount of an analyte in a sample can be an amount greater than a control or normal level of the analyte normally present in the sample.

[0015] As used herein, the term "about" refers to a quantitative measurement that does not include the mass measurement of ions and refers to ± 10% of the indicated value.

[0016] The above description of the summary of the invention is non-limiting and other features and advantages of the invention will be apparent from the following detailed description of the invention and the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A Figure 2 shows the relationship between insulin resistance, BMI, and LS-IM score SSPG concentrations according to tertiles (T) of LS-IM score on the x-axis (T1: <-5.7, T2: >=-5.7 to <8.04, T3: >=8.04) and tertiles of BMI in the panels (T1: <27.7 kg / m 2 , T2: >27.7 to <=32.1 kg / m 2 , T3:>32.1kg / m 2 ). Figure 1B Figure 2 shows the relationship between insulin resistance, BMI, and TG / HDL-C SSPG concentrations according to tertiles (T) of LS-IM score on the x-axis (T1: <-5.7, T2: >=-5.7 to <8.04, T3: >=8.04) and tertiles of BMI in the panel (T1: <27.7 kg / m2, T2: >27.7 to <=32.1 kg / m 2 , T3:>32.1kg / m 2 ). Figure 1C Figure 2 shows the relationship between insulin resistance, BMI, and LS-IM score + TG / HDL-CSSPG concentrations according to tertiles (T) of LS-IM score on the x-axis (T1: <-5.7, T2: >=-5.7 to <8.04, T3: >=8.04) and tertiles of BMI in the panels (T1: <27.7 kg / m 2 , T2: >27.7 to <=32.1 kg / m 2 , T3:>32.1kg / m 2). The number of subjects is printed below each box plot. SSPG concentration is a direct measure of insulin resistance, with higher SSPG concentrations indicating a greater degree of insulin resistance than lower SSPG concentrations. LS-IM score = -8.8*intermediate VLDL–19*small IDL+11*large type a LDL+10.2*intermediate LDL+14.8*very small type b LDL–16.8*very small type c LDL+8.6*very small type d LDL+7.9*small HDL, all LS-IM values ​​are in standard deviation units and the standard deviations are 15.8, 50.3, 92.6, 8.1, 62.6, 34.6, 19.5 and 3267 nmol / L, respectively. LS-IM score + TG / HDL-C = -8.8*intermediate VLDL–19*small IDL+11*large type a LDL+10.2*intermediate LDL+14.8*very small type b LDL–16.8*very small type c LDL+8.6*very small type d LDL+7.9*small HDL+17.1*TG / HDL-C, all LS-IM values ​​are in standard deviation units and the standard deviations are 15.8, 50.3, 92.6, 8.1, 62.6, 34.6, 19.5, 3267 nmol / L, respectively, TG / HDL-C is expressed in standard deviation units and the standard deviation is 17.1. SSPG: steady-state plasma glucose. LS-IM: ion mobility-based subfractionation. HDL: high-density lipoprotein. LDL: low-density lipoprotein. IDL: intermediate-density lipoprotein. VLDL: very low-density lipoprotein. TG / HDL-C: ratio of triglyceride to high-density lipoprotein cholesterol concentration.

[0018] Figure 2A Receiver operating characteristic curves predicting SSPG concentrations in the top tertile (>196 mg / dL) are shown: TG / HDL-C, LS-IM score, and TG / HDL-C + LS-IM score. Figure 2B Receiver operating characteristic curves predicting SSPG concentrations in the top tertile (>196 mg / dL) are shown: BMI, BMI+TG / HDL-C, and BMI+TG / HDL-C+LS-IM score. Figure 2CReceiver operating characteristic curves predicting SSPG concentrations in the top tertile (>196 mg / dL) are shown: BMI+TG / HDL-C+sex+race+ethnicity and BMI+TG / HDL-C+sex+race+ethnicity+LS-IM score. LS-IM score = -8.8*intermediate VLDL–19*small IDL+11*large a-type LDL+10.2*intermediate LDL+14.8*very small b-type LDL–16.8*very small c-type LDL+8.6*very small d-type LDL+7.9*small HDL. TG / HDL-C+LS-IM score = 17.1*TG / HDL-C+LS-IM score. BMI+TG / HDL-C = 33.5*BMI+17.1*TG / HDL-C. BMI+TG / HDL-C=33.5*BMI+17.1*TG / HDL-C+LS-IM score. BMI+TG / HDL+sex+race+ethnicity=33.5*BMI+17.1*TG / HDL-C–12*Male+15.7*Hispanic+5.3*Native American+21.5*East Asian-6*Black+21*South Asian. BMI+TG / HDL+sex+race+ethnicity+LS-IM score=33.5*BMI+17.1*TG / HDL-C–12*Male+15.7*Hispanic+5.3*Native American+21.5*East Asian-6*Black+21*South Asian+LS-IM score. SSPG: steady-state plasma glucose. LS-IM: ion mobility-based lipoprotein subfractionation. HDL: high-density lipoprotein. LDL: low-density lipoprotein. IDL: Intermediate-density lipoprotein. VLDL: Very low-density lipoprotein. TG / HDL-C: The ratio of triglyceride to high-density lipoprotein cholesterol concentrations.

[0019] Figure 3 A flow chart showing the study inclusion criteria. DETAILED DESCRIPTION

[0020] Disclosed herein are methods for diagnosing or predicting insulin resistance in patients in need thereof (e.g., patients with diabetes and / or prediabetes). For example, without being bound by theory, individuals in a given population whose steady-state plasma glucose (SSPG) concentration is in the top tertile may be defined as having insulin resistance.

[0021] In some embodiments, if the SSPG concentration is ≥190 mg / dL, the individual has insulin resistance. In some embodiments, if the SSPG concentration is ≥195 mg / dL, such as >196 mg / dL, the individual has insulin resistance. In some embodiments, if the SSPG concentration is ≥198 mg / dL, the individual has insulin resistance. In some embodiments, if the SSPG concentration is ≥200 mg / dL, the individual has insulin resistance. In some embodiments, if the SSPG concentration is ≥205 mg / dL, the individual has insulin resistance.

[0022] Suitable test samples for the methods of the present invention include any test sample that may contain the analyte of interest. In some preferred embodiments, the sample is a biological sample; that is, a sample obtained from any biological source (such as an animal, cell culture, organ culture, etc.). In certain preferred embodiments, the sample is obtained from a mammal (such as a dog, a cat, a horse, etc.). Particularly preferred mammals are primates, most preferably male or female humans. Preferred samples include body fluids such as blood, plasma, serum, saliva, cerebrospinal fluid, or tissue samples; preferably plasma and serum. Such samples can be obtained, for example, from a patient (i.e., a living person, male or female, who appears in a clinical setting for diagnosis, prognosis, or treatment of a disease or condition).

[0023] In certain embodiments, the methods provided herein include lipoprotein subfractions in a sample by ion mobility. For example, the levels of lipoprotein subfractions (lipoprotein analysis) can be measured according to the following: U.S. Patent Application Publication No. 2008 / 0305549 and Mora S. et al. Circulation. December 8, 2015; 132(23):2220-9, each of which is incorporated herein by reference.

[0024] In some embodiments, lipoprotein subfractions include determining the amount of one or more or all of VLDL (very low density lipoprotein), IDL (intermediate density lipoprotein), LDL (low density lipoprotein) and HDL (high density lipoprotein). In some embodiments, lipoprotein subfractions include determining the amount of one or more or all of the following: intermediate VLDL (very low density lipoprotein), small IDL (intermediate density lipoprotein), large a-type LDL (low density lipoprotein), intermediate LDL (low density lipoprotein), very small b-type LDL (low density lipoprotein), very small c-type LDL (low density lipoprotein), very small d-type LDL (low density lipoprotein) and small HDL (high density lipoprotein).

[0025] In some embodiments, the present invention provides an insulin resistance score (RS) and / or a probability of insulin resistance P (IR) based on the level of lipoprotein subfractions measured by the methods provided herein. In some embodiments, the insulin resistance score (RS) can be determined by formula A1.

[0026] Formula A1:

[0027] RS = LS-IM score = -8.8*intermediate VLDL–19*small IDL+11*large type a LDL+10.2*intermediate LDL+14.8*very small type b LDL–16.8*very small type c LDL+8.6*very small type d LDL+7.9*small HDL

[0028] In certain embodiments, the method provided herein includes measuring triglyceride (TG) level and / or high-density lipoprotein cholesterol (HDL-C) level.Therefore, in certain embodiments, the level of lipoprotein subfractions measured by the method provided herein and triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) levels provide insulin resistance score (RS) and / or the probability P (IR) of insulin resistance.In certain embodiments, insulin resistance score (RS) can be determined by formula A2.The level of triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) can be measured according to methods known in the art.

[0029] Formula A2:

[0030] RS=LS-IM score+TG / HDL-C=LS-IM score+17.1*TG / HDL-C=-8.8*Intermediate VLDL-

[0031] 19* small IDL + 11* large type a LDL + 10.2* intermediate type LDL + 14.8* very small type b LDL – 16.8*

[0032] Very small c-type LDL + 8.6* Very small d-type LDL + 7.9* Small HDL + 17.1*TG / HDL-C

[0033] In certain embodiments, method provided herein includes measuring body mass index (BMI), or combining sex, race and ethnicity to measure body mass index (BMI).Therefore, in certain embodiments, this paper provides insulin resistance score (RS) and / or the probability P (IR) of insulin resistance based on the level of lipoprotein subfraction measured by method provided herein and triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) level and combining sex, race, ethnicity and body mass index (BMI) measured by standard method.In certain embodiments, insulin resistance score (RS) can be determined by formula A3.The level of triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) can be measured according to methods known in the art.

[0034] Formula A3:

[0035] RS = LS-IM score + BMI + TG / HDL-C + gender + race + ethnicity = LS-IM score + 33.5*BMI +

[0036] 17.1*TG / HDL-C – 12*Male + 15.7*Hispanic + 5.3*Native American + 21.5*East Asian - 6*Black + 21*South Asian = -8.8*Intermediate VLDL – 19*Small IDL + 11*Large a-LDL + 10.2*Intermediate LDL + 14.8*Very small b-LDL – 16.8*Very small c-LDL + 8.6*Very small d-LDL +

[0037] 7.9*Small HDL+33.5*BMI+17.1*TG / HDL-C – 12*Male+15.7*Hispanic+5.3*Native American+21.5*East Asian-6*Black+21*South Asian

[0038] Note: In formula A3, if the subject is male, the variable male = 1, if the subject is female, the variable male = 0; if the subject is Spanish, the variable Spanish = 1, otherwise the variable Spanish = 0; if the subject is Native American, the variable Native American = 1, otherwise the variable Native American = 0; if the subject is East Asian, the variable East Asian = 1, otherwise the variable East Asian = 0; if the subject is Southern Black, the variable Southern Black = 1, otherwise the variable Southern Black = 0;

[0039] The variable Asian=1 if the subject is Asian, otherwise the variable Asian=0.

[0040] In certain embodiments, insulin resistance is diagnosed according to the level of lipoprotein subfractions measured by the method provided herein. In certain embodiments, insulin resistance is diagnosed by the level of lipoprotein subfractions measured by the method provided herein and triglycerides (TG) and high-density lipoprotein C (HDL-C) levels. In certain embodiments, insulin resistance is diagnosed by the level of lipoprotein subfractions measured by the method provided herein and triglycerides (TG) and high-density lipoprotein C (HDL-C) levels and in combination with sex, race, ethnicity and body mass index (BMI) measured by standard methods.

[0041] In certain embodiments, the methods described herein provide an insulin resistance score (eg, according to Formula A1, Formula A2, or Formula A3).

[0042] In certain embodiments, the methods described herein provide the probability of developing insulin resistance.

[0043] In certain embodiments, the biological samples provided herein include plasma or serum samples.

[0044] Examples

[0045] Example 1: Quantification of Lipoprotein Subfractions by Ion Mobility

[0046] We evaluated the usefulness of fasting lipoprotein subfractions (LS) in identifying individuals with IR.

[0047] Lipid panel, LS based on ion mobility (LS-IM), and IR based on steady-state plasma glucose (SSPG) concentration were assessed in 526 adult volunteers without diabetes. IR was defined as being in the highest tertile of SSPG concentration. LS-IM scores were calculated by linear combinations of regression coefficients from stepwise regression analysis with SSPG concentration as the dependent variable. Scores were also calculated for LS-IM scores + TG / HDL-C and for models containing gender, race, ethnicity, BMI, TG / HDL-C, and LS-IM scores. IR prediction was assessed by the area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV), with the top 5% of scores considered positive tests.

[0048] The LS-IM score and TG / HDL-C predicted IR similarly (AUC = 0.68; PPV = 0.59 and AUC = 0.70; PPV = 0.59, respectively), and prediction was improved when LS-IM was combined with: TG / HDL-C (AUC = 0.73; PPV = 0.70); TG / HDL-C and BMI (AUC = 0.82; PPV = 0.81); and TG / HDL-C, BMI, sex, race, and ethnicity (AUC = 0.84; PPV = 0.89).

[0049] The LS-IM score and TG / HDL-C were comparable for identifying individuals with IR, and their combination with sex, race, ethnicity, and BMI further improved IR prediction over TG / HDL-C alone. In patients who have undergone IM testing, the LS-IM score may help prioritize subjects for further evaluation and intervention to reduce IR.

[0050] Insulin resistance (IR) increases the risk of type 2 diabetes (T2D) and atherosclerotic cardiovascular disease (ASCVD). However, IR is rarely measured in healthy individuals in clinical settings because the techniques used to directly measure IR are labor-intensive and expensive. Indirect methods for IR assessment have not been validated. Although some patients with clear indications for assessing T2D risk may have their IR assessed by their clinicians, many other patients may remain unaware of their elevated IR measurements and possible increased risk of T2D and ASCVD.

[0051] Multiple clinical measures have the potential to help identify individuals with IR. Body mass index (BMI) is strongly associated with IR, but not all patients with insulin resistance are obese. IR is also associated with lipid and lipoprotein abnormalities, including high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C) concentrations, and a predominance of small, dense low-density lipoprotein (LDL) particles. The TG to HDL-C concentration ratio (TG / HDL-C) can be used to identify individuals with insulin resistance. Lipoprotein size and LS concentration have also been used to identify patients with IR. In this setting, IR scores based on nuclear magnetic resonance (NMR)-derived lipoprotein information have been shown to have strong correlations with multiple measures of IR. LS can also be measured by ion mobility. LS quantified by NMR is related to, but not identical to, LS quantified by ion mobility. Ion mobility-based methods are direct measures of lipoprotein particle counts based on lipoprotein particle size, while NMR is an algorithm-derived measurement.

[0052] The association of ion mobility-based LS (LS-IM) with direct measures of IR has not been reported. Finding a strong association may provide patients and physicians with additional information about IR-driven T2D and ASCVD risk. Therefore, we set out to describe the relationship between LS-IM and direct measures of IR measured during insulin inhibition testing and determine the usefulness of LS for identifying individuals with insulin resistance.

[0053] Research Design and Methods

[0054] Study population: This cross-sectional analysis included 526 participants from 1072 apparently healthy individuals who volunteered to participate in IR studies between 1999 and 2011. Participants were recruited from the San Francisco Bay Area through advertisements in local newspapers. Pregnant women, individuals aged 79 years or younger than 18 years, those with a history of cardiovascular disease, and patients with diabetes requiring insulin therapy were excluded from the studies. For this analysis, we excluded 149 participants with fasting glucose ≥126 mg / dL and 397 participants with missing data on at least one of the following measures: race, ethnicity, body mass index (BMI), TG, HDL-C, LDL cholesterol, systolic blood pressure, diastolic blood pressure, alanine aminotransferase, or any ion mobility LS ( Figure 3 ).

[0055] The Institutional Review Board approved all studies, and all participants provided written informed consent.

[0056] Clinical and measurement

[0057] Study visits were conducted at the Stanford University Clinical and Translational Research Center. Race and ethnicity were self-reported. Height and weight were measured without shoes and wearing light clothing, and BMI was calculated by dividing weight (in kilograms) by height (in meters) squared. Blood pressure was measured by an automated blood pressure recorder after participants sat quietly in a chair for 5 minutes with their feet on the floor and their arms supported at heart level. Three blood pressure measurements were obtained at 1-minute intervals using an appropriately sized cuff and averaged.

[0058] Insulin suppression test

[0059] The extent of IR was directly measured by a modified and validated version of the insulin suppression test (IST), which quantifies the ability of physiological hyperinsulinemia to homeostatically stimulate glucose uptake.

[0060] After an overnight fast, an intravenous catheter was placed in each arm. One arm was used to draw blood samples, while the other arm received a continuous infusion of octreotide acetate (0.27 μg / m 2 / min), insulin (32mU / m 2 / min) and glucose (267mg / m 2 / min) for 180 minutes. Blood samples were collected every 30 minutes for 150 minutes and then every 10 minutes to measure steady-state plasma insulin (SSPI) and glucose (SSPG) concentrations.

[0061] During IST, octreotide acetate inhibits endogenous insulin secretion, and insulin infusion results in similar SSPI concentrations in all individuals (physiological hyperinsulinemia). The ability of physiological hyperinsulinemia to stimulate the uptake of infused glucose is represented by the SSPG concentration. The higher the SSPG concentration, the lower the insulin-stimulated glucose uptake and the more insulin resistant a person is. The IR measured during IST is highly correlated with the IR measured during a normoglycemic hyperinsulinemic clamp test. Individuals with SSPG concentrations in the highest tertile were defined as insulin resistant. This decision was based on the results of a prospective study in which subjects in the highest tertile of SSPG concentrations were more likely to develop ASCVD than those in the lowest tertile of SSPG concentrations.

[0062] Lipid and lipoprotein measurements

[0063] The lipid panel was assessed after an overnight fast at the Stanford Health Care Clinical Laboratory, and LDL cholesterol was calculated using the Friedewald equation.

[0064] Ion mobility measurements

[0065] LS levels were assessed by ion mobility at Quest Diagnostics Nichols Institute (San Juan Capistrano, CA). and Mora S, Caulfield MP, Wohlgemuth J, Chen Z, Superko HR, Rowland CM, Glynn RJ, Ridker PM, Krauss RM. Atherogenic lipoprotein subfractions determined by ion mobility and first cardiovascular events after random allocation to high-intensity statin or placebo: the justification for the use of statins prevention: an intervention trial evaluating rosuvastatin (JUPITER)Trial. Issue 2015; 132:2220–9]. LS and their definitions are provided in Table 3 .

[0066] Statistical methods:

[0067] Pearson's correlation coefficient (r) was used as a measure of pairwise correlation. The association of TG / HDL-C and each LS-IM measure with SSPG concentration was assessed in a separate linear regression model adjusted for age, sex, race, ethnicity and BMI. In order to incorporate multiple ion mobility variables and covariates into a single model, a backward stepwise regression model was performed using the Aikaike Information Criterion (AIC) as a measure of the comparison model. In the regression model, candidate variables were age, sex, race, ethnicity, BMI, TG / HDL-C and LS-IM measures, and the dependent variable was SSPG concentration. Using the regression coefficients for LS-IM measures in the final stepwise model, the ion mobility score (LS-IM score) was calculated. The score for each subject is a linear combination of the LS-IM variables from the stepwise model, calculated as follows: B1*Var1+B2*Var2+…+Bp*Varp, where B1 to Bp are regression coefficients, and Var1 to Varp are subject-specific values ​​for p LS-IM variables in the final model. In a similar manner, scores were calculated for other combinations of variables from the model: 1) LS-IM score + TG / HDL-C; 2) all non-ion mobility variables (sex, race, ethnicity, BMI, and TG / HDL-C); and 3) all variables in the full model (sex, race, ethnicity, BMI, TG / HDL-C, and LS-IM score). When included in the regression model, all continuous variables were standardized by conversion to standard deviation (SD) units. Tertiles of LS-IM scores (calculated using ion mobility coefficients from stepwise models) were plotted against SSPG concentrations, in tertiles of BMI for women and men.

[0068] Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) and 95% confidence interval were calculated for each of the above scores using the Delong method. The difference in AUC was assessed for paired ROC curves using the Delong method. [DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44(3): 837–45.] When the top 5% of the scores were considered positive tests, the positive predictive value (PPV) of each score was determined to identify individuals in the top tertile of SSPG concentrations. The confidence interval for the PPV was calculated using the Wilson method. [Wilson EB. Probable Inference, the Law of Succession, and Statistical Inference. J Am Statistical Assoc 1927; 22: 158. https: / / doi.org / 10.1080 / 01621459.1927.10502953.209-212]. The Bonferroni method was used to determine the significance level adjusted for multiple comparisons. [Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ 1995; 310: 6973. https: / / doi.org / 10.1136 / bmj.310.6973.170 (Clinical research editor) 170].

[0069] All analyses were performed using the R programming language. [RC Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2020]

[0070] result

[0071] The median age of the study participants was 50 years and approximately two-thirds (65%) were female (Table 4). Most subjects were non-Hispanic (92%) and 70% were white. Nearly half (48%) of the participants were obese (BMI ≥ 30.0 kg / m 2), 38% of the participants were overweight (BMI 25.0 to 29.9 kg / m 2 ).

[0072] SSPG concentrations were found to be positively correlated with both BMI and TG / HDL-C (r = 0.54 and 0.32, respectively) (Table 5).

[0073] For all LS-IM measures, pairwise correlations (r) between BMI and various LS-IM measures were less than 0.2 (in absolute magnitude) and the only measure that reached statistical significance (p-value < 0.0008) was HDL-L, which was negatively correlated with BMI (r = -0.17). Pairwise correlations between SSPG concentrations and individual LS-IM measures that reached statistical significance (p-value < 0.0008) included: negative correlations between SSPG concentrations and LDL peak particle size (r = -0.26), small IDL (r = -0.21), large HDL (r = -0.18), and large type a LDL (r = -0.17); and positive correlations between SSPG concentrations and small LDL (r = 0.27), very small type a LDL (r = 0.23), total LDL (r = 0.19), intermediate LDL (r = 0.19), and very small type b LDL (r = 0.17). Stronger correlations were found between TG / HDL-C and LS-IM measures: Significant correlations were found for the following measures (p-values ​​< 0.0008): TG / HDL-C was negatively correlated with: LDL peak particle size (r = -0.72), large a-type LDL (r = -0.50), small IDL (r = -0.38), large b-type LDL (r = -0.37), large HDL (r = -0.33), total HDL (r = -0.18); And TG / HDL-C was positively correlated with the following: very small b-type LDL (r=0.64), very small a-type LDL (r=0.62), very small c-type LDL (r=0.60), small LDL (r=0.50), large VLDL (r=0.46), very small d-type LDL (r=0.38), intermediate VLDL (r=0.35), all LDL (r=0.32) and all non-HDL (r=0.23).

[0074] Most LS-IM measures were associated with significant changes in SSPG concentrations, as evidenced by confidence intervals that did not cross zero (Table 6). The largest changes per SD in the LS-IM measures were in LDL Peak Particle Size (SSPG concentration decreased by 16.7 mg / dL for each 1 SD increase in Peak Particle Size) and LDL Small (SSPG concentration increased by 15.8 mg / dL for each 1 SD increase in LDL Small Particle Number). Large effect sizes were also found for the non-LS-IM measures of TG / HDL-C and BMI. Similarly, SSPG concentration increased by 17 mg / dL for each 1 SD increase in TG / HDL-C. In these same models, SSPG concentration increased by an average of 40 mg / dL for each 1 SD increase in BMI.

[0075] The eight LS-IM measures, along with BMI, TG / HDL-C, sex, race, and ethnicity, were the final variables retained in the backward stepwise regression analysis with SSPG concentration as the dependent variable ( Table 1 ). Tertiles of LS-IM scores showed strong positive associations across all tertiles of BMI in women and in the lower two tertiles of BMI in men ( Figure 1A ), and the relationships looked similar when plotting tertiles of TG / HDL-C or tertiles of scores representing the combination of LS-IM+TG / HDL-C ( Figure 1B and 1C ).

[0076] Receiver operating characteristic (ROC) curves for predicting IR (SSPG concentration in the top tertile) were plotted for: LS-IM score, TG / HDL-C, and LS-IM score + TG / HDL-C ( Figure 2A ); BMI, BMI+TG / HDL-C and LS-IM score+BMI+TG / HDL-C( Figure 2B ); BMI+TG / HDL-C+sex+race+ethnicity and LS-IM score+BMI+TG / HDL-C+sex+race+ethnicity ( Figure 2C). The combination of LS-IM score + TG / HDL-C improved the prediction of IR compared with TG / HDL-C alone (AUC = 0.73 vs. 0.70, respectively; p-value < 0.0001) (Table 2). Similarly, the LS-IM score + BMI + TG / HDL-C improved prediction compared with BMI + TG / HDL-C alone (AUC = 0.82 vs. 0.79, respectively; p-value < 0.0001), and the LS-IM score also improved prediction when added to BMI + TG / HDL-C + sex + race + ethnicity (AUC = 0.84 vs. 0.81, respectively; p-value < 0.0001). In the subset of non-obese individuals, adding the LS-IM score to TG / HDL-C, BMI + TG / HDL-C, and BMI + TG / HDL-C + sex + race + ethnicity also improved prediction (p-value < 0.0001) (Table 2).

[0077] When the top 5% values ​​were considered for each of the same set of variables, the positive predictive value (PPV) was calculated to identify subjects in the top tertile of SSPG concentrations. The PPV ranged from 59% when only TG / HDL-C or IM score was considered to 89% when the full model of LS-IM score + BMI + TG / HDL-C + sex + race + ethnicity was considered (Table 2).

[0078] in conclusion

[0079] There are three main findings from this study of LS and IR. First, the LS-IM had a weak to moderate correlation with a direct measure of IR (SSPG concentration). Second, the prediction of IR by the LS-IM was similar to that by TG / HDL-C. Third, the prediction of IR was improved when the LS-IM score was combined with TG / HDL-C and was further improved by adding sex, race, ethnicity, and BMI.

[0080] Reaven first described and proposed the role of IR in the pathogenesis of T2D and ASCVD and its association with metabolic abnormalities, including elevated TG concentrations and low HDL-C concentrations, in 1988. Subsequent work by Reaven and colleagues and others has confirmed the association between IR and multiple metabolic abnormalities and various clinical syndromes, including nonalcoholic fatty liver disease, obstructive sleep apnea, polycystic ovary syndrome, and certain types of cancer with detrimental health consequences. Therefore, the identification of IR in individuals is of clinical importance because it can promote changes in behavior and clinical management to reduce the risks associated with IR.

[0081] Since direct methods to measure IR are not feasible in clinical settings, there is a need for robust and validated clinical measures of IR to identify individuals at risk for adverse outcomes of IR. In this study, we aimed to address this need by determining the usefulness of LS-IM, as measured by ion mobility, for identifying individuals with IR.

[0082] IR dyslipidemia is characterized by elevated TG and low HDL-C concentrations, as well as a predominance of small, dense LDL, postprandial lipidemia, and elevated concentrations of partially oxidized LDL. Several lipid and LS abnormalities measured by NMR have also been found in patients with IR. Consistent with these previously reported findings, we demonstrated that some LS-IM measures were associated with IR (SSPG concentrations). Specifically, we found that SSPG concentrations were associated with increased numbers of large VLDL particles, intermediate to very small LDL particles, and large HDL particles, and were associated with smaller peak particle sizes and fewer numbers of small IDL particles, large LDL particles, and large HDL particles. From a pathophysiological perspective, these associations are thought to be due in part to increased hepatic production and decreased clearance of VLDL from plasma, as well as increased hepatic lipase activity and subsequent hydrolysis of phospholipids in LDL and HDL particles, resulting in smaller and denser LDL particles, a decrease in large HDL particles, and an increase in small HDL particles.

[0083] We also demonstrated that the incorporation of LS-IM improves the prediction of IR measured by SSPG concentration. Specifically, when predicting individuals in the first tertile of SSPG concentration, the AUC and PPV of the LS-IM score alone and TG / HDL-C alone were similar, but when used together, they significantly improved the AUC and PPV (Table 2). In addition, compared with the score excluding the LS-IM score and only including gender, race, ethnicity, BMI and TG / HDL-C, the score obtained from the full stepwise model including the LS-IM score with gender, race, ethnicity, BMI and TG / HDL-C significantly improved the AUC and PPV. It is difficult to compare the LS-IM score described here with the LP-IR score derived from NMR described previously. The LP-IR score is based on HOMA-IR as a measure of IR, while the current score is based on SSPG concentration (a direct measure of IR). In addition, the size range of the defined area between the two scores is also different. However, both scores have demonstrated that particles of various size ranges are independently associated with IR.

[0084] We also demonstrated that simpler measures of IR, such as TG / HDL-C and BMI, as well as the LS-IM score alone, perform well with respect to the prediction of IR. Specifically, we observed that the TG / HDL-C ratio was similar to the LS-IM score and that BMI was similar to the combination of the LS-IM score and TG / HDL-C with respect to the prediction of IR. We have previously demonstrated that TG / HDL-C can be used to identify individuals with insulin resistance. The TG / HDL-C ratio is a simple measure that can be used to identify individuals with increased cardiometabolic risk and is preferable in clinical or research settings where LS-IM measurements are not possible. Our results also confirm the potential usefulness of the LS-IM score for identifying individuals with the greatest degree of insulin resistance and higher levels of dyslipidemia in either the obese or non-obese groups. Figure 1A Figure 1B and Figure 1C As shown in Table 2, within each tertile of BMI, individuals with higher scores (LS-IM, TG / HDL-C, and LS-IM+TG / HDL-C) generally had greater insulin resistance (higher SSPG concentrations) than those with lower LS-IM scores. As shown in Table 2, the AUC for predicting IR in non-obese individuals was not reduced for LS-IM score, TG / HDL-C, or their combination. This observation is consistent with our previous findings that, at a given BMI, insulin-resistant individuals have higher TG and lower HDL-C concentrations than insulin-sensitive individuals.

[0085] Strengths of our study include the fact that we validated the usefulness of the LS-IM score for predicting IR using the gold standard measure of IR. In addition, we improved risk prediction using already available data from LS-IM clinical testing where those measurements were available (at no additional cost). Limitations of our study include that the individuals in our study were not typical of the population undergoing LS-IM testing. The individuals studied were ostensibly healthy volunteers, and individuals undergoing LS-IM testing were primarily referred by their clinicians for testing to assess the risk of ASCVD. Further studies evaluating the LS-IM score in patients undergoing LS-IM testing will be needed to assess the contribution of IR and associated dyslipidemia to ASCVD risk.

[0086] In summary, LS-IM measurements in addition to TG / HDL-C and / or BMI can also improve the prediction of IR. In individuals who have undergone LS-IM testing, this information can be used to prioritize lifestyle interventions to improve IR and reduce associated T2D and ASCVD risks. Targeted interventions including increased exercise and weight loss have been shown to be particularly helpful in improving IR and reducing individuals' progression to T2D. This information can also be used to identify individuals who may be candidates for further testing (such as through other validated measures, such as fasting insulin or IR scores) and ultimately identify individuals who may not have known their IR and corresponding higher risk of T2D and ASCVD.

[0087] Table 1: Results of stepwise linear regression analysis

[0088] variable β 95% CI P-value BMI 33.5 28.4 to 38.6 8.71E-33 Male (reference = female) -12.0 -23.1 to -1 0.03 Hispanic (reference = non-Hispanic) 15.7 -2.2 to 33.7 0.09 Race (reference = White) Native Americans 5.3 -39.8 to 50.4 0.82 East Asians 21.5 3.9 to 39.2 0.017 Black people -6.0 -24.9 to 12.8 0.53 South Asians 21.0 6.2 to 35.7 0.006 TG / HDL-C 17.1 9.8 to 24.4 5.86E-06 Intermediate VLDL -8.8 -15.6 to -2.1 0.011 Small IDL -19.0 -30 to -8.1 6.95E-04 Large alpha LDL 11.0 -0.7 to 22.6 0.065 Intermediate LDL 10.2 4.5 to 15.9 4.49E-04 Very small b-LDL 14.8 5.1 to 24.6 0.003 Very small c-LDL -16.8 -28.4 to -5.1 0.005 Very small d-LDL 8.6 0.4 to 16.8 0.041 Small HDL 7.9 2.2 to 13.6 0.007

[0089] β represents the change in SSPG concentration (mg / dL) per one SD change (continuous variables) or relative to a reference category (categorical variables).

[0090] SDs for continuous variables were: BMI=5.4, TG / HDL-C=2.45, intermediate VLDL=15.8, small IDL=50.3, large a-LDL=92.6, intermediate LDL=8.1, very small b-LDL=62.6, very small c-LDL=34.6, very small d-LDL=19.5, and small HDL=3267, all lipoprotein measurements are in nmol / L.

[0091] R-squared of the model = 0.43

[0092] HDL: High-density lipoprotein

[0093] LDL: Low-density lipoprotein

[0094] IDL: Intermediate density lipoprotein

[0095] VLDL: Very low density lipoprotein

[0096] Table 2: Area under the ROC curve (AUC) and positive predictive value (PPV) for predicting individuals in the highest tertile of SSPG concentration (>196 mg / dL)

[0097]

[0098]

[0099] *P value comparing the AUC of the current row to the row immediately above. a) Indicates that the p-values ​​for 6 statistical tests in the adjusted table are significant (<0.008)

[0100] ┼ The top 5% values ​​were considered the PPV for a positive test. b) The sample size of N=13 in the top 5% of non-obese subjects was too small to obtain a reliable PPV

[0101] R-squared for the model with continuous SSPG concentration (mg / dL)

[0102] LS-IM score = -8.8*intermediate VLDL–19*small IDL+11*large type a LDL+10.2*intermediate LDL+14.8*very small type b LDL–16.8*very small type c LDL+8.6*very small type d LDL+7.9*small HDL

[0103] LS-IM score + TG / HDL-C = LS-IM score + 17.1*TG / HDL-C

[0104] BMI+TG / HDL-C=33.5*BMI+17.1*TG / HDL-C

[0105] BMI+TG / HDL-C+sex+race+ethnicity=33.5*BMI+17.1*TG / HDL-C–12*Male+15.7*Hispanic+5.3*Native American+21.5*East Asian6*Black+21*South Asian

[0106] SSPG: steady-state plasma glucose

[0107] TG / HDL-C: ratio of triglycerides to high-density lipoprotein cholesterol

[0108] LS-IM: ion mobility-based lipoprotein subfractionation

[0109] HDL: High-density lipoprotein

[0110] LDL: Low-density lipoprotein

[0111] IDL: Intermediate density lipoprotein

[0112] VLDL: Very low density lipoprotein

[0113] Table 3: Size range of lipoprotein subfractions based on ion mobility

[0114]

[0115] *Particle size with the largest particle count observed in the LDL region

[0116] Table 4: Participant characteristics

[0117]

[0118]

[0119]

[0120] Value is N (%) or median [range]

[0121] Table 5: Pairwise Pearson correlation coefficients for SSPG, BMI, and ion mobility-based lipoprotein subfractions

[0122]

[0123]

[0124] a: p value < 0.0008; b: p value < 0.01;

[0125] Table 6: Results of linear regression models showing the relationship of triglycerides / HDL-C and individual ion mobility-based lipoprotein subfractions to steady-state plasma glucose concentration (SSPG)

[0126] variable β 95% CI P-value Triglycerides / HDL-C 17.0 11.9 to 22.2 2.43E-10 All non-HDL 6.4 1.2 to 11.6 0.017 Large VLDL 6.2 0.9 to 11.5 0.022 Intermediate VLDL 2.0 -3.2 to 7.3 0.45 Small VLDL -4.2 -9.4 to 1 0.11 Large IDL 0.7 -4.4 to 5.9 0.78 Small IDL -12.8 -18.1 to -7.5 2.46E-06 LDL peak particle size -16.7 -22 to -11.3 1.95E-09 Total LDL 10.5 5.3 to 15.7 8.40E-05 Large alpha LDL -11.1 -16.4 to -5.9 4.24E-05 Large b-LDL -3.3 -8.5 to 1.9 0.21 Intermediate to small LDL 13.7 8.6 to 18.9 2.62E-07 Intermediate LDL 8.4 3.3 to 13.5 0.0014 Small LDL 15.8 10.6 to 21 5.39E-09 Very small LDL 14.7 9.4 to 20 8.17E-08 Very small b-LDL 11.8 6.5 to 17.1 1.59E-05 Very small c-LDL 8.8 3.5 to 14 0.0012 Very small d-LDL 7.9 2.8 to 13 0.0026 All HDL 0.4 -4.9 to 5.8 0.88 Large HDL -8.3 -13.8 to -2.7 0.0038 Small HDL 3.6 -1.6 to 8.9 0.18

[0127] Beta: change in SSPG concentration per 1 SD increase in variable estimated in separate models adjusted for age, sex, race, ethnicity, and body mass index

[0128] The contents of the articles, patents, and patent applications mentioned or cited herein, and all other documents and electronically available information are hereby incorporated by reference in their entirety to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference. Applicants reserve the right to physically incorporate into this application any and all materials and information from any such articles, patents, patent applications, or other physical and electronic documents.

[0129] The methods illustratively described herein can be appropriately practiced in the absence of any one or more elements, one or more limitations not specifically disclosed herein. Therefore, for example, the terms "comprise", "include", "contain", etc. should be understood extensively and are not limited. In addition, the terms and expressions adopted herein have been used as descriptive and non-restrictive terms, and there is no intention to use such terms and expressions to exclude any equivalents or parts thereof of the features shown and described. It should be recognized that various modifications are possible within the scope of the present invention. Therefore, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modifications and variations of the invention implemented therein disclosed herein may be adopted by those skilled in the art, and such modifications and variations are considered to be within the scope of the present invention.

[0130] The invention is described broadly and generally herein. Each of the narrower species and subgeneric subgeneric groupings falling within the general disclosure also forms part of the method. This comprises the general description of the method with the proviso or negative limitation that any subject matter is removed from the genus, regardless of whether the deleted material is specifically recited herein.

[0131] Other embodiments are within the following claims.In addition, where features or aspects of the methods are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

Claims

1. A method for diagnosing or predicting insulin resistance in a patient in need thereof, the method comprising determining the amount of lipoprotein subfractions in a sample by ion mobility.

2. The method of claim 1, wherein the method further comprises measuring triglyceride (TG) levels.

3. The method of claim 1 or claim 2, wherein the method further comprises measuring high-density lipoprotein cholesterol (HDL-C) levels.

4. The method of any one of claims 1 to 3, wherein the method further comprises measuring body mass index (BMI) in conjunction with sex, race, and ethnicity.

5. The method of claim 1, wherein the method further comprises measuring triglyceride (TG) levels and high-density lipoprotein cholesterol (HDL-C) levels.

6. The method of claim 1, wherein the method further comprises measuring triglyceride (TG) levels, high-density lipoprotein cholesterol (HDL-C) levels, and body mass index (BMI) in combination with sex, race, and ethnicity.

7. The method according to any one of claims 1 to 6, wherein the method provides an insulin resistance score.

8. The method according to any one of claims 1 to 6, wherein the method provides the probability of developing insulin resistance.

9. The method according to any one of claims 1 to 8, wherein the sample comprises a plasma or serum sample.

10. The method according to any one of claims 1 to 9, wherein determining the amount of lipoprotein subfractions comprises determining the amount of one or more or all of the following: VLDL (very low density lipoprotein), IDL (intermediate density lipoprotein), LDL (low density lipoprotein) and HDL (high density lipoprotein).

11. The method according to any one of claims 1 to 10, wherein determining the amount of lipoprotein subfractions comprises determining the amount of one or more or all of the following: intermediate VLDL (very low density lipoprotein), small IDL (intermediate density lipoprotein), large a-type LDL (low density lipoprotein), intermediate LDL (low density lipoprotein), very small b-type LDL (low density lipoprotein), very small c-type LDL (low density lipoprotein), very small d-type LDL (low density lipoprotein) and small HDL (high density lipoprotein).

12. A method for determining the amount of a lipoprotein subfraction in a sample, the method comprising determining the amount of the lipoprotein subfraction in the sample by ion mobility.

13. The method of claim 12, wherein the method further comprises measuring triglyceride (TG) levels.

14. The method of claim 12 or claim 13, wherein the method further comprises measuring high-density lipoprotein cholesterol (HDL-C) levels.

15. The method of any one of claims 12 to 14, wherein the method further comprises measuring body mass index (BMI) in conjunction with sex, race, and ethnicity.

16. The method of claim 12, wherein the method further comprises measuring triglyceride (TG) levels and high-density lipoprotein cholesterol (HDL-C) levels.

17. The method of claim 12, wherein the method further comprises measuring triglyceride (TG) levels, high-density lipoprotein cholesterol (HDL-C) levels, and body mass index (BMI) in conjunction with sex, race, and ethnicity.

18. The method according to any one of claims 12 to 17, wherein the method provides an insulin resistance score.

19. The method of any one of claims 12 to 17, wherein the method provides a probability of developing insulin resistance.

20. The method of any one of claims 12 to 19, wherein the sample comprises a plasma or serum sample.

21. The method of any one of claims 12 to 20, wherein determining the amount of lipoprotein subfractions comprises determining the amount of one or more or all of the following: VLDL (very low density lipoprotein), IDL (intermediate density lipoprotein), LDL (low density lipoprotein) and HDL (high density lipoprotein).

22. The method of any one of claims 12 to 21, wherein determining the amount of lipoprotein subfractions comprises determining the amount of one or more or all of the following: intermediate VLDL (very low density lipoprotein), small IDL (intermediate density lipoprotein), large a-type LDL (low density lipoprotein), intermediate LDL (low density lipoprotein), very small b-type LDL (low density lipoprotein), very small c-type LDL (low density lipoprotein), very small d-type LDL (low density lipoprotein) and small HDL (high density lipoprotein).

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